Department of Ultrasound, Guanghua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Department of Radiology, Guanghua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Front Immunol. 2024 Aug 1;15:1417156. doi: 10.3389/fimmu.2024.1417156. eCollection 2024.
Quantitatively assess the severity and predict the mortality of interstitial lung disease (ILD) associated with Rheumatoid arthritis (RA) was a challenge for clinicians. This study aimed to construct a radiomics nomogram based on chest computed tomography (CT) imaging by using the ILD-GAP (gender, age, and pulmonary physiology) index system for clinical management.
Chest CT images of patients with RA-ILD were retrospectively analyzed and staged using the ILD-GAP index system. The balanced dataset was then divided into training and testing cohorts at a 7:3 ratio. A clinical factor model was created using demographic and serum analysis data, and a radiomics signature was developed from radiomics features extracted from the CT images. Combined with the radiomics signature and independent clinical factors, a nomogram model was established based on the Rad-score and clinical factors. The model capabilities were measured by operating characteristic curves, calibration curves and decision curves analyses.
A total of 177 patients were divided into two groups (Group I, n = 107; Group II, n = 63). Krebs von den Lungen-6, and nineteen radiomics features were used to build the nomogram, which showed favorable calibration and discrimination in the training cohort [AUC, 0.948 (95% CI: 0.910-0.986)] and the testing validation cohort [AUC, 0.923 (95% CI: 0.853-0.993)]. Decision curve analysis demonstrated that the nomogram performed well in terms of clinical usefulness.
The CT-based radiomics nomogram model achieved favorable efficacy in predicting low-risk RA-ILD patients.
定量评估类风湿关节炎（RA）相关间质性肺病（ILD）的严重程度并预测其死亡率对临床医生来说是一项挑战。本研究旨在通过使用ILD-GAP（性别、年龄和肺生理学）指数系统构建基于胸部 CT 成像的放射组学列线图，用于临床管理。
回顾性分析 RA-ILD 患者的胸部 CT 图像，并使用 ILD-GAP 指数系统进行分期。然后将平衡数据集按 7:3 的比例分为训练和测试队列。使用来自 CT 图像的放射组学特征创建放射组学特征，并使用来自 CT 图像的放射组学特征创建放射组学特征。使用放射组学特征和独立临床因素相结合，基于 Rad-score 和临床因素建立列线图模型。通过接受者操作特征曲线、校准曲线和决策曲线分析来评估模型性能。
共有 177 例患者分为两组（I 组，n=107；II 组，n=63）。使用 Krebs von den Lungen-6 和 19 个放射组学特征构建了列线图，该列线图在训练队列（AUC，0.948[95%置信区间：0.910-0.986]）和测试验证队列（AUC，0.923[95%置信区间：0.853-0.993]）中具有良好的校准和区分能力。决策曲线分析表明，该列线图在临床实用性方面表现良好。
基于 CT 的放射组学列线图模型在预测低风险 RA-ILD 患者方面具有良好的疗效。
